DETAILED ACTION
Notice of Pre-AIA or AIA Status
The present application, filed on or after March 16, 2013, is being examined under the first inventor to file provisions of the AIA . Claims 1-20 are pending.
Continued Examination Under 37 CFR 1.114
A request for continued examination (RCE) under 37 CFR 1.114, including the fee set forth in 37 CFR 1.17(e), was filed in this application after final rejection. Since this application is eligible for continued examination under 37 CFR 1.114, and the fee set forth in 37 CFR 1.17(e) has been timely paid, the finality of the previous Office action has been withdrawn pursuant to 37 CFR 1.114. Applicant's RCE submission filed on 6/2/26, with claims corresponding to 5/4/26, has been entered.
Claim Rejections - 35 USC § 101
35 U.S.C. 101 reads as follows:
Whoever invents or discovers any new and useful process, machine, manufacture, or composition of matter, or any new and useful improvement thereof, may obtain a patent therefor, subject to the conditions and requirements of this title.
Claims 1-20 are rejected under 35 U.S.C. 101 because the claimed invention is directed to a judicial exception without significantly more.
Step 1 (The Statutory Categories): Is the claim to a process, machine, manufacture, or composition of matter? MPEP 2106.03.
Per Step 1, claim 1 is to a method (i.e., a process), claim 10 to a system (i.e., a machine), and claim 16 to a non-transitory computer-readable medium (i.e., a manufacture or machine). Thus, the claims are directed to statutory categories of invention. However, the claims are rejected under 35 U.S.C. 101 because they are directed to an abstract idea, a judicial exception, without reciting additional elements that integrate the judicial exception into a practical application.
The analysis proceeds to Step 2A Prong One.
Step 2A Prong One: Does the claim recite an abstract idea, law of nature, or natural phenomenon? MPEP 2106.04.
The abstract idea of claims 1 and 16 is (claim 1 being representative):
receiving system data corresponding to an engine system;
determining a plurality of remaining useful life (RUL) values based on the system data, each RUL value of the plurality of RUL values associated with a component or system of the engine system;
determining that a first RUL value of the plurality of RUL values is less than a service interval threshold corresponding to a planned service event;
generating a near-term service recommendation comprising a first component that corresponds to the first RUL value;
determining that a second RUL value of the plurality of RUL values is greater than the service interval threshold;
generating an extended term service recommendation comprising a second component that corresponds to the second RUL value;
receiving duty cycle information associated with a third component, the duty cycle information comprising a run time of the third component of the engine system;
generating a coordinated service recommendation by dynamically populating one or more fields of the coordinated service recommendation based on the near-term service recommendation, the extended term service recommendation, and the degradation value; and
providing the coordinated service recommendation to a user.
The abstract idea of claim 10 is:
receiving system data corresponding to a plurality of engine system;
determining one or more remaining useful life (RUL) values and duty cycle information based on the system data;
determining that a RUL value of the one or more RUL values is less than a service interval threshold corresponding to a planned service event;
determining one or more degradation values up to the planned service event based on the duty cycle information;
determining a priority value for each of the plurality of engine systems;
generating a service recommendation by dynamically populating one or more fields of the service recommendation, the one or more fields comprising an ordered list of engine systems of the plurality of engine systems according to the priority value; and
providing the service recommendation to a user.
The abstract idea steps italicized above are those which could be performed mentally, including with pen and paper. The steps describe, at a high level, receiving system data, analyzing the data (i.e., generating a service recommendation), and outputting the result. These are all steps an administrator could perform, with the aid of pen and paper. If a claim limitation, under its broadest reasonable interpretation, covers performance of the limitation in the mind, including observations, evaluations, judgements, and/or opinions, then it falls within the Mental Processes – Concepts Performed in the Human Mind grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the business relation or sales activity regarding generating service recommendations, which constitutes a process that, under its broadest reasonable interpretation, covers commercial activity. This is further supported by [0002] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers commercial interactions, including contracts, legal obligations, advertising, marketing, sales activities or behaviors, and/or business relations, then it falls within the Certain Methods of Organizing Human Activity – Commercial or Legal Interactions grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Additionally and alternatively, the abstract idea steps italicized above describe the rules or instructions regarding generating service recommendations, which constitutes a process that, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people. This is further supported by [0002] of applicant’s specification as filed. If a claim limitation, under its broadest reasonable interpretation, covers managing personal behavior relationships, interactions between people, including social activities, teaching, and/or following rules or instructions, then it falls within the Certain Methods of Organizing Human Activity – Managing Personal Behavior Relationships, Interactions Between People grouping of abstract ideas. Accordingly, the claim recites an abstract idea.
Step 2A Prong Two: Does the claim recite additional elements that integrate the judicial exception into a practical application? MPEP 2106.04.
Claim 1 recites the following additional elements: by at least one processing circuit of a computing system; [user] device; by a machine learning model.
Claim 10 recites the following additional elements: a processing circuit having one or more processors and memory storing instructions; [user] device; by a machine learning model.
Claim 16 recites the following additional elements: a non-transitory computer-readable media storing instructions; one or more processors of a processing circuit; [user] device; by a machine learning model.
These elements are merely instructions to apply the abstract idea to a computer and/or described in a results-oriented manner, per MPEP 2106.05(f). Applicant has only described generic computing elements in their specification, as seen in [0056]-[0064] of applicant’s specification as filed, for example.
Further, the combination of these elements is nothing more than a generic computing system applied to the tasks of the abstract idea. Because the additional elements are merely instructions to apply the abstract idea to a generic computing system, they do not integrate the abstract idea into a practical application, when viewed in combination. See MPEP 2106.05(f).
Therefore, per Step 2A Prong Two, the additional elements, alone and in combination, do not integrate the judicial exception into a practical application. The claim is directed to an abstract idea.
Step 2B (The Inventive Concept): Does the claim recite additional elements that amount to significantly more than the judicial exception? MPEP 2106.05.
Step 2B involves evaluating the additional elements to determine whether they amount to significantly more than the judicial exception itself.
The examination process involves carrying over identification of the additional element(s) in the claim from Step 2A Prong Two and carrying over conclusions from Step 2A Prong Two pertaining to MPEP 2106.05(f).
The additional elements and their analysis are therefore carried over: applicant has merely recited elements that facilitate the tasks of the abstract idea, as described in MPEP 2106.05(f).
Further, the combination of these elements is nothing more than a generic computing system. When the claim elements above are considered, alone and in combination, they do not amount to significantly more.
Therefore, per Step 2B, the additional elements, alone and in combination, are not significantly more. The claims are not patent eligible.
The analysis takes into consideration all dependent claims as well:
Dependent claims 2-9, 11-15, and 17-20 include additional abstract steps and/or information that merely narrow the abstract idea above. There are no further additional elements to consider, beyond those highlighted above. This simple narrowing of the abstract idea does not translate into patent eligibility.
Accordingly, claims 1-20 are rejected under 35 USC § 101 as being directed to non-statutory subject matter.
Claim Rejections - 35 USC § 103
The following is a quotation of 35 U.S.C. 103 which forms the basis for all obviousness rejections set forth in this Office action:
A patent for a claimed invention may not be obtained, notwithstanding that the claimed invention is not identically disclosed as set forth in section 102, if the differences between the claimed invention and the prior art are such that the claimed invention as a whole would have been obvious before the effective filing date of the claimed invention to a person having ordinary skill in the art to which the claimed invention pertains. Patentability shall not be negated by the manner in which the invention was made.
The factual inquiries for establishing a background for determining obviousness under 35 U.S.C. 103 are summarized as follows:
1. Determining the scope and contents of the prior art.
2. Ascertaining the differences between the prior art and the claims at issue.
3. Resolving the level of ordinary skill in the pertinent art.
4. Considering objective evidence present in the application indicating obviousness or nonobviousness.
Claims 1 and 7-16 are rejected under 35 U.S.C. 103 as being unpatentable over Wetzer (US 20020143421) in view of Wu (US 20210072742) and Tang (US 20220284278).
Claims 1 and 16
Wetzer discloses:
[A method {[0001]} comprising:]
[A non-transitory computer-readable media storing instructions that, when executed by one or more processors of a processing circuit {[0022]}, cause the one or more processors to perform operations comprising:]
receiving, by at least one processing circuit of a computing system, system data corresponding to an engine system
{receiving, by at least one processing circuit of a computing system, system data corresponding to an engine system indicated in [0026]: The data processing system 44 uses configuration data to organize components and track components of the equipment in a coherent manner. The configuration data (e.g., component identifiers) may be used to index different components in the databases (e.g., the first database 26) that require maintenance for a particular equipment or group of similar equipment.
corresponding to an engine system also indicated in [0002]: Equipment refers to any device, apparatus, machine, electronics, or assembly that requires maintenance or provides improved performance or greater longevity upon receipt of such maintenance. Equipment means any mechanical equipment, any electrical equipment, any data processing system, any electronics or optical equipment, any software associated with mechanical equipment, electrical equipment, electronic equipment or a data processing system. A component may represent a part, an assembly of parts, a subassembly of a part, an element, or another constituent of a equipment.
by at least one processing circuit of a computing system also indicated in [0022]: The data processor 34 may include one or more microprocessors, electronic memory supporting the functionality of the microprocessor or microprocessors, and software instruction modules stored in the microprocessor or microprocessors.};
determining, by the at least one processing circuit, that a first RUL value of the plurality of RUL values is less than a service interval threshold corresponding to a planned service event
{determining that a first RUL value of the plurality of RUL values is less than a service interval threshold indicated in [0025]: Configuration data may include manufacturing data. The supplier data source 12 may provide manufacturing data concerning a component. For example, the supplier data source 12 may provide longevity reference data on a component of the equipment or the equipment. The longevity reference data indicates the expected life span of a component. During the expected life span, the component meets or exceeds a threshold reliability criteria. The threshold reliability criteria may refer to a percentage of availability of the equipment or a component of the equipment. In one embodiment, the supplier data source 12 may provide longevity reference data, component serial numbers, component identifiers, component descriptions, manufacturer data on a component or other data that is suitable for input to the component longevity estimator 36.
less than a service interval threshold also indicated in [0034]: In contrast, if the usage data indicates that the component or equipment has a lesser usage rate (e.g., a significantly lesser usage rate) than the assumed usage rate, the longevity estimator 36 may increase the reference longevity to a revised reference longevity. As a result, the remaining estimated reliable life span preferably provides a realistic and reliable estimate of performance of the mechanical equipment under actual operating conditions because the revised longevity reference data considers the usage data. Therefore, the expiration date or the longevity data may be modified based on the collection of usage data from sensors 51 on or affiliated with the equipment.
corresponding to a planned service event indicated in [0086]: In one embodiment, a predictive maintenance controller 336 uses at least one of the longevity estimate and the probability of failure to tentatively schedule a proposed activity or a proposed plan of predictive maintenance.};
generating, by the at least one processing circuit, a near-term service recommendation comprising a first component that corresponds to the first RUL value
{generating a near-term service recommendation comprising a first component that corresponds to the first RUL value indicated in [0036]: Returning to FIG. 1, the scheduler 40 defines maintenance activities which are expressed as planned maintenance data. Planned maintenance data refers to what maintenance should take place and when the maintenance should take place based on the estimated remaining reliable life span of the component or the equipment and an installation date of the component on the equipment.};
determining, by the at least one processing circuit, that a second RUL value of the plurality of RUL values is greater than the service interval threshold
{determining that a second RUL value of the plurality of RUL values is greater than the service interval threshold indicated in [0061]: Once the threshold probability of failure is satisfied consistent with one or more of the above conditions, that data processing system 144 assigns a maintenance activity and planned maintenance time period to the component for storage in a planned maintenance database. In general, the scheduler 40 may assign a higher or lower priority to the scheduling of maintenance for components of the same equipment based on one or more of the following factors: the relative cost of the components, relative labor costs for installation of the components, relative availability of the components, relative probabilities of failures of the components, and the relative impacts on performance and safety of the equipment associated with the components.};
generating, by the at least one processing circuit, an extended term service recommendation comprising a second component that corresponds to the second RUL value
{generating an extended term service recommendation indicated in [0037]: Further, a safeguard interval prior to the expiration of the remaining estimated life span may be added to compensate for potential delays and inefficiencies in the component procurement process, labor shortages, or both.
comprising a second component that corresponds to the second RUL value indicated in [0055]: In one example, the predictor 136 may determine reliability data or failure data for a second component in the same equipment or in different equipment than a first component if the second component is substantially similar to the first component and if the second component is present in an analogous technical environment to the first component.}.
Wetzer doesn't explicitly disclose, however, Wu, in a similar field of endeavor directed to predictive maintenance, teaches:
generating, by the at least one processing circuit, a coordinated service recommendation by dynamically populating one or more fields of the coordinated service recommendation based on the near-term service recommendation, the extended term service recommendation, and the degradation value
{generating a coordinated service recommendation by dynamically populating one or more fields of the coordinated service recommendation based on the near-term service recommendation, the extended term service recommendation, and the degradation value indicated in [0145]: In some embodiments, MPM system 602 includes a data analytics and visualization platform. MPM system 602 may provide a web interface which can be accessed by service technicians 620, client devices 448, and other systems or devices. The web interface can be used to access the equipment performance information, view the results of the optimization, identify which equipment is in need of maintenance, and otherwise interact with MPM system 602. Service technicians 620 can access the web interface to view a list of equipment for which maintenance is recommended by MPM system 602. Service technicians 620 can use the equipment purchase and maintenance recommendations to proactively repair or replace connected equipment 610 in order to achieve the optimal cost predicted by the objective function J. These and other features of MPM system 602 are described in greater detail below.
Also see [0290]: Further, data aggregator 1110 may perform various manipulations on the data to ensure the data is in a correct format(s) (e.g., converted from analog to binary signals, converted into JSON objects, etc.), in a correct order (e.g., chronological order), and/or otherwise manipulated based on needs of short-term optimizer 1114 and/or long-term optimizer 1112.
Also see [0294] and [0300], which describe recommendations based on a degradation value.};
providing, by the at least one processing circuit, the coordinated service recommendation to a user device
{providing the coordinated service recommendation to a user device indicated in [0301]: The final schedule can be provided by schedule combiner 1116 to BMS 606 via communications interface 1108.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Wetzer to include the features of Wu. Given that Wetzer is directed to maintenance planning, one of ordinary skill in the art would have been motivated to look to Wu, in order to facilitate determining a maintenance and replacement strategy for equipment, one in which the maintenance and replacement strategy indicates optimal times for maintenance and/or replacement of equipment to occur in order to optimize (e.g., reduce) costs over an optimization period {[0059] of Wu}.
While examiner asserts that limitations describing the engine system are disclosed by Wetzer (see citations above), for the purposes of compact prosecution, examiner looks to an additional reference. Tang, in a similar field of endeavor directed to estimating remaining useful life based on operation and degradation characteristics, teaches:
corresponding to an engine system
{engine system indicated in [0054]: [0054] Processing proceeds to operation S258, where the computing system (e.g., server computer 200 of FIG. 1 or the like) can obtain an indication of an asset class associated with and/or representative of the obtained asset data, for example, an asset class relevant to the set of assets to be analyzed (e.g., for remaining useful life predictions, etc.). For example, in some embodiments, assets may be associated with different asset classes, such as pumps; heating, venting, and air conditioning (HVAC); conveyor systems; engines; and/or other asset classes.}.
The combination of Wetzer and Wu also doesn’t explicitly teach, however, Tang teaches:
determining, by a machine learning model, a plurality of remaining useful life (RUL) values based on the system data, each RUL value of the plurality of RUL values associated with a component or system of the engine system
{determining, by a machine learning model, a plurality of remaining useful life (RUL) values based on the system data, each RUL value of the plurality of RUL values associated with a component or system of the engine system indicated in [0072]: FIG. 5 depicts a block diagram of an example workflow 500 to train models for use in predicting remaining useful life of assets, according to embodiments of the present invention. As illustrated in FIG. 5, operations included in workflow 500 begin at input block 504 where asset time series data for asset(s) to be analyzed (e.g., in a predictive maintenance system, for remaining useful life prediction, etc.) is obtained/acquired. In some embodiments, the asset time series data can be provided for preprocessing. The preprocessing can include, for example, decomposing time series data into chronological cycles, duration of operation, and/or the like at an initial preprocessing block 506. The preprocessing can also include selecting and scaling features that are pertinent for the estimation/prediction of remaining useful life at a subsequent preprocessing block 508. The preprocessed data 510 can be provided for use in training model(s) (e.g., remaining useful life prediction models, predictive maintenance models, etc.) and/or generating remaining useful life prediction data for one or more assets.};
receiving, by the at least one processing circuit, duty cycle information associated with a third component, the duty cycle information comprising a run time of the third component of the engine system
{receiving duty cycle information associated with a third component, the duty cycle information comprising a run time of the third component indicated in [0072]: The preprocessing can include, for example, decomposing time series data into chronological cycles, duration of operation, and/or the like at an initial preprocessing block 506. The preprocessing can also include selecting and scaling features that are pertinent for the estimation/prediction of remaining useful life at a subsequent preprocessing block 508. The preprocessed data 510 can be provided for use in training model(s) (e.g., remaining useful life prediction models, predictive maintenance models, etc.) and/or generating remaining useful life prediction data for one or more assets. A scoring function can be tuned at tuning block 512, for example based on criticality of prediction, priority of asset, and/or the like, such that the scoring function can be used in weighting parameters (e.g., hyperparameters, etc.) of a neural network (e.g., LSTM neural network, etc.).};
determining, by the at least one processing circuit, a degradation value up to the planned service event based on the duty cycle information
{determining a degradation value up to the planned service event based on the duty cycle information also indicated in [0069], where degradation value up to the planned service event represented by degradation factor: In some embodiments, predictive maintenance system 402 can include data preprocessing 404 which can provide for preprocessing obtained asset data (e.g., time series data, IoT time series sensor data, etc.) that is to be provided as input to a model (e.g., neural network LSTM model, etc.), for example, to decompose the time series data into chronological cycles and duration of operation, to better represent asset degradation as input into a model, and/or the like.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Wetzer and Wu to include the features of Tang. Given that Wetzer is directed to maintenance planning, one of ordinary skill in the art would have been motivated to look to Tang, in order to streamline the development and deployment of novel models that assist in optimizing remaining useful life predictions towards the end of life of an asset {[0015]-[0016] of Tang}.
Claim 7
Wetzer further discloses: generating, by the at least one processing circuit, a downtime prediction based on a downtime value of the second component, wherein the extended term service recommendation further includes the downtime prediction {[0008]}.
Claim 8
Wetzer further discloses: receiving, by the at least one processing circuit, a first near-term group of RUL values responsive to determining that the first RUL value is less than or equal to the service interval threshold, wherein the near-term service recommendation further comprises a first list of components that correspond to each RUL value in the first near-term group of RUL values {[0036], [0061]}.
Claim 9
Wetzer further discloses: receiving, by the at least one processing circuit, a first extended term group of RUL values responsive to determining that the second RUL value of the plurality of RUL values is greater than the service interval threshold, wherein the extended term service recommendation further comprises a second list of components that correspond to each RUL value in the first extended term group of RUL values {[0037], [0061]}.
Claim 10
Wetzer discloses:
A computing system {[0001]} comprising:
a processing circuit having one or more processors and memory storing instructions that, when executed by the one or more processors {[0022]}, cause the one or more processors to perform operations comprising:
receiving system data corresponding to a plurality of engine systems
{receiving system data corresponding to a plurality of engine systems indicated in [0026]: The data processing system 44 uses configuration data to organize components and track components of the equipment in a coherent manner. The configuration data (e.g., component identifiers) may be used to index different components in the databases (e.g., the first database 26) that require maintenance for a particular equipment or group of similar equipment.
corresponding to a plurality of engine systems also indicated in [0002]: Equipment refers to any device, apparatus, machine, electronics, or assembly that requires maintenance or provides improved performance or greater longevity upon receipt of such maintenance. Equipment means any mechanical equipment, any electrical equipment, any data processing system, any electronics or optical equipment, any software associated with mechanical equipment, electrical equipment, electronic equipment or a data processing system. A component may represent a part, an assembly of parts, a subassembly of a part, an element, or another constituent of a equipment.};
determining that a RUL value of the one or more RUL values is less than a service interval threshold corresponding to a planned service event
{determining that a RUL value of the one or more RUL values is less than a service interval threshold indicated in [0025]: Configuration data may include manufacturing data. The supplier data source 12 may provide manufacturing data concerning a component. For example, the supplier data source 12 may provide longevity reference data on a component of the equipment or the equipment. The longevity reference data indicates the expected life span of a component. During the expected life span, the component meets or exceeds a threshold reliability criteria. The threshold reliability criteria may refer to a percentage of availability of the equipment or a component of the equipment. In one embodiment, the supplier data source 12 may provide longevity reference data, component serial numbers, component identifiers, component descriptions, manufacturer data on a component or other data that is suitable for input to the component longevity estimator 36.
less than a service interval threshold also indicated in [0034]: In contrast, if the usage data indicates that the component or equipment has a lesser usage rate (e.g., a significantly lesser usage rate) than the assumed usage rate, the longevity estimator 36 may increase the reference longevity to a revised reference longevity. As a result, the remaining estimated reliable life span preferably provides a realistic and reliable estimate of performance of the mechanical equipment under actual operating conditions because the revised longevity reference data considers the usage data. Therefore, the expiration date or the longevity data may be modified based on the collection of usage data from sensors 51 on or affiliated with the equipment.
corresponding to a planned service event indicated in [0086]: In one embodiment, a predictive maintenance controller 336 uses at least one of the longevity estimate and the probability of failure to tentatively schedule a proposed activity or a proposed plan of predictive maintenance.};
determining a priority value for each of the plurality of engine systems
{determining a priority value for each of the plurality of engine systems indicated in [0061]: Once the threshold probability of failure is satisfied consistent with one or more of the above conditions, that data processing system 144 assigns a maintenance activity and planned maintenance time period to the component for storage in a planned maintenance database. In general, the scheduler 40 may assign a higher or lower priority to the scheduling of maintenance for components of the same equipment based on one or more of the following factors: the relative cost of the components, relative labor costs for installation of the components, relative availability of the components, relative probabilities of failures of the components, and the relative impacts on performance and safety of the equipment associated with the components. For example, the scheduler 40 may assign a higher priority to the scheduling of maintenance for a component with a higher probability of failure than other components of the equipment. Alternatively, the scheduler 40 may assign a higher priority to the condition or maintenance work where there is a high cost (e.g., economic or otherwise) of failure, even if the probability of failure is lower. }.
Wetzer doesn’t explicitly disclose, however, Wu, in a similar field of endeavor directed to predictive maintenance, teaches:
generating a service recommendation by dynamically populating one or more fields of the service recommendation, the one or more fields comprising an ordered list of engine systems of the plurality of engine systems according to the priority value
{generating a service recommendation by dynamically populating one or more fields of the service recommendation, the one or more fields comprising an ordered list of engine systems of the plurality of engine systems according to the priority value indicated in [0145]: In some embodiments, MPM system 602 includes a data analytics and visualization platform. MPM system 602 may provide a web interface which can be accessed by service technicians 620, client devices 448, and other systems or devices. The web interface can be used to access the equipment performance information, view the results of the optimization, identify which equipment is in need of maintenance, and otherwise interact with MPM system 602. Service technicians 620 can access the web interface to view a list of equipment for which maintenance is recommended by MPM system 602. Service technicians 620 can use the equipment purchase and maintenance recommendations to proactively repair or replace connected equipment 610 in order to achieve the optimal cost predicted by the objective function J. These and other features of MPM system 602 are described in greater detail below.
Also see [0290]: Further, data aggregator 1110 may perform various manipulations on the data to ensure the data is in a correct format(s) (e.g., converted from analog to binary signals, converted into JSON objects, etc.), in a correct order (e.g., chronological order), and/or otherwise manipulated based on needs of short-term optimizer 1114 and/or long-term optimizer 1112.};
providing the service recommendation to a user device
{providing the coordinated service recommendation to a user device indicated in [0301]: The final schedule can be provided by schedule combiner 1116 to BMS 606 via communications interface 1108.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify Wetzer to include the features of Wu. Given that Wetzer is directed to maintenance planning, one of ordinary skill in the art would have been motivated to look to Wu, in order to facilitate determining a maintenance and replacement strategy for equipment, one in which the maintenance and replacement strategy indicates optimal times for maintenance and/or replacement of equipment to occur in order to optimize (e.g., reduce) costs over an optimization period {[0059] of Wu}.
While examiner asserts that limitations describing the engine system are disclosed by Wetzer (see citations above), for the purposes of compact prosecution, examiner looks to an additional reference. Tang, in a similar field of endeavor directed to estimating remaining useful life based on operation and degradation characteristics, teaches:
corresponding to a plurality of engine systems
{engine system indicated in [0054]: [0054] Processing proceeds to operation S258, where the computing system (e.g., server computer 200 of FIG. 1 or the like) can obtain an indication of an asset class associated with and/or representative of the obtained asset data, for example, an asset class relevant to the set of assets to be analyzed (e.g., for remaining useful life predictions, etc.). For example, in some embodiments, assets may be associated with different asset classes, such as pumps; heating, venting, and air conditioning (HVAC); conveyor systems; engines; and/or other asset classes.}.
The combination of Wetzer and Wu also doesn’t explicitly teach, however, Tang teaches:
determining, by a machine learning model, one or more remaining useful life (RUL) values and duty cycle information based on the system data
{determining, by a machine learning model, one or more remaining useful life (RUL) values and duty cycle information based on the system data indicated in [0072]: FIG. 5 depicts a block diagram of an example workflow 500 to train models for use in predicting remaining useful life of assets, according to embodiments of the present invention. As illustrated in FIG. 5, operations included in workflow 500 begin at input block 504 where asset time series data for asset(s) to be analyzed (e.g., in a predictive maintenance system, for remaining useful life prediction, etc.) is obtained/acquired. In some embodiments, the asset time series data can be provided for preprocessing. The preprocessing can include, for example, decomposing time series data into chronological cycles, duration of operation, and/or the like at an initial preprocessing block 506. The preprocessing can also include selecting and scaling features that are pertinent for the estimation/prediction of remaining useful life at a subsequent preprocessing block 508. The preprocessed data 510 can be provided for use in training model(s) (e.g., remaining useful life prediction models, predictive maintenance models, etc.) and/or generating remaining useful life prediction data for one or more assets.};
determining one or more degradation values up to the planned service event based on the duty cycle information
{determining one or more degradation values up to the planned service event based on the duty cycle information also indicated in [0069], where degradation value up to the planned service event represented by degradation factor: In some embodiments, predictive maintenance system 402 can include data preprocessing 404 which can provide for preprocessing obtained asset data (e.g., time series data, IoT time series sensor data, etc.) that is to be provided as input to a model (e.g., neural network LSTM model, etc.), for example, to decompose the time series data into chronological cycles and duration of operation, to better represent asset degradation as input into a model, and/or the like.}.
It would have been obvious to one of ordinary skill in the art before the effective filing date of the invention to modify the combination of Wetzer and Wu to include the features of Tang. Given that Wetzer is directed to maintenance planning, one of ordinary skill in the art would have been motivated to look to Tang, in order to streamline the development and deployment of novel models that assist in optimizing remaining useful life predictions towards the end of life of an asset {[0015]-[0016] of Tang}.
Claim 11
Wetzer further discloses: wherein generating the service recommendation further comprises populating the one or more fields of the service recommendation with an indication of parts needed to complete a service event {[0038]}.
Claim 12
Wetzer further discloses: wherein a first priority value for a first engine system of the plurality of engine systems is based on: an age of first engine system; a total run time for a first component of the first engine system; and a RUL value for the first component of the first engine system {[0028]}.
Claim 13
Wetzer further discloses: wherein the first priority value is further based on a second priority value of a second engine system, the second engine system located proximate the first engine system, and a difference between the first priority value and the second priority value at or below a predetermined threshold {[0051], [0073]}.
Claim 14
Wetzer further discloses: wherein the operations further comprise determining a predicted downtime value for a first engine system of the plurality of engine systems based on predicted downtimes of engine systems having higher priority values than the first engine system {[0008]}.
Claim 15
Wetzer further discloses: wherein the predicted downtime value is further based on a set of components that are recommended to be serviced, a travel time for the first engine system, a travel time for a technician, and a delivery time for service parts, such that the predicted downtime value for the first engine system is an aggregate of one or more of a service time for the set of components, the travel time for the first engine system, the travel time for the technician, the delivery time for service parts, and the predicted downtimes of engine systems having a higher priority value than the first engine system {[0008], [0061]}.
No Prior Art Rejection Applied to Claims 2-6 and 17-20
There is no prior art rejection applied to claims 2-6 and 17-20. Applicant is directed to the non-final rejection mailed 10/17/25 for examiner’s explanation.
Response to Arguments
Applicant's arguments filed 5/4/26 have been fully considered. The headings and page numbers below correspond to those used by applicant.
Claim Rejections Under 35 U.S.C. § 101
Regarding the remarks on page 10, examiner notes that applicant appears to conflate the abstract idea with the additional elements, when offering comments such as “The use of a machine learning model to determine RUL values from system data involves computational processes that are inherently beyond the capability of the human mind. Likewise, the multi-factor approach of determining a degradation value based on duty cycle information and then dynamically populating fields of a coordinated service recommendation based on near- term service recommendations, extended term service recommendations, and the degradation value together constitutes a specific, technical data processing pipeline, not an abstract idea.”
Examiner’s position, however, is that the additional elements of the claims, including “machine learning,” are generic computing elements claimed in a results-oriented manner that merely facilitate the tasks of the abstract idea. Per MPEP 2106.05(f), this does not demonstrate integration into practical application and/or add significantly more.
Applicant continues on pages 10-11 regarding Step 2A Prong Two, highlighting that the claims present “an improvement to the technical field of engine system service event planning.” Applicant then points to sections of the specification for support, also remarking that “the Office Action improperly considered whether the additional elements are well-understood, routine, and conventional under Step 2A Prong Two.”
Examiner first notes that the terms “well-understood, routine, and conventional” were not used by the examiner at Step 2A Prong Two or Step 2B. Instead, examiner posited that the additional elements are generic computing elements that merely facilitate the tasks of the abstract idea, per MPEP 2106.05(f). Applicant’s specification is generally supportive of this interpretation, as seen, e.g., in [0056]-[0064] of applicant’s specification as filed, where the computing devices are generically described.
Applicant continues on pages 12-13 regarding Step 2B, offering: “These elements, considered individually and as an ordered combination, are not generic computer components performing routine functions. Rather, they represent a specific, unconventional arrangement of technical steps that achieves the technical improvement to engine system service recommendations.”
However, the determination made at Step 2B, similar to Step 2A Prong Two, is informed by the additional elements, analyzed alone and in combination. In this instance and in accordance with MPEP 2106, examiner carried over the analysis performed at Step 2A Prong Two, i.e., the additional elements are generic computing elements that merely facilitate the tasks of the abstract idea, per MPEP 2106.05(f). Whether viewed alone or in combination, this does not integrate the abstract idea into practical application.
Accordingly, examiner maintains the rejections under 35 U.S.C. § 101.
Claim Rejections Under 35 U.S.C. § 103
With respect to applicant’s remarks concerning the rejections under 35 U.S.C. § 103, examiner notes that they are predicated on the current claim amendments, which necessitated the new grounds of rejection above. Instead of restating here, examiner directs applicant to the claim analysis above.
Accordingly, examiner maintains the rejections under 35 U.S.C. § 103.
In summary, examiner has responded to all of applicant’s remarks.
Conclusion
The prior art made of record and not relied upon is considered pertinent to applicant's disclosure:
“A Directed Acyclic Graph Network Combined With CNN and LSTM for Remaining Useful Life Prediction” (NPL attached), which teaches: To overcome this drawback, the proposed method generates a short-term sequence by sliding the time window (TW) with one step size. In addition, based on the degradation mechanism, the piece-wise RUL function is used instead of the traditional linear function. In the experimental test, the turbofan engine degradation simulation dataset provided by NASA is used to validate the proposed RUL prediction model. By comparing with the existing methods using the same dataset, it can be concluded that the prediction method proposed in this paper has better prediction capability.
US 20200301408, which teaches: A model predictive maintenance (MPM) system for building equipment includes one or more processing circuits having one or more processors and memory. The memory store instructions that, when executed by the one or more processors, cause the one or more processors to perform operations including estimating a degradation state of the building equipment, using a degradation impact model to predict an amount of one or more input resources consumed by the building equipment to produce one or more output resources based on the degradation state of the building equipment, generating a maintenance schedule for the building equipment based on the amount of the one or more input resources predicted by the degradation impact model, and initiating a maintenance activity for the building equipment in accordance with the maintenance schedule.
US 20230123527, which teaches: A client-server system that performs machine learning based information fusion to predict part failure likelihood is described. The system receives transactional data pertaining to replacement of, and sensor data pertaining to duty cycle of, one or more parts.
Any inquiry concerning this communication or earlier communications from the examiner should be directed to JOHN SAMUEL WASAFF whose telephone number is (571)270-5091. The examiner can normally be reached Monday through Friday 8:00 am to 6:00 pm.
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JOHN SAMUEL WASAFF
Primary Examiner
Art Unit 3629
/JOHN S. WASAFF/Primary Examiner, Art Unit 3629
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